\Header{gnlsObject}{Fitted gnls Object}
\keyword{models}{gnlsObject}
\begin{Description}\relax
An object returned by the \code{gnls} function, inheriting from class
\code{gnls} and also from class \code{gls}, and representing a
generalized nonlinear least squares fitted model. Objects of this
class have methods for the generic functions  \code{anova},
\code{coef}, \code{fitted}, \code{formula}, \code{getGroups},
\code{getResponse}, \code{intervals}, \code{logLik}, \code{plot},
\code{predict}, \code{print}, \code{residuals}, \code{summary}, and
\code{update}.\end{Description}
\begin{Value}
The following components must be included in a legitimate \code{gnls}
object. 
\begin{ldescription}
\item[\code{apVar}] an approximate covariance matrix for the
variance-covariance coefficients. If \code{apVar = FALSE} in the list
of control values used in the call to \code{gnls}, this
component is equal to \code{NULL}.
\item[\code{call}] a list containing an image of the \code{gnls} call that
produced the object.
\item[\code{coefficients}] a vector with the estimated nonlinear model
coefficients.
\item[\code{contrasts}] a list with the contrasts used to represent factors
in the model formula. This information is important for making
predictions from a new data frame in which not all levels of the
original factors are observed. If no factors are used in the model,
this component will be an empty list.
\item[\code{dims}] a list with basic dimensions used in the model fit,
including the components \code{N} - the number of observations used in
the fit and \code{p} - the number of coefficients in the nonlinear
model.
\item[\code{fitted}] a vector with the fitted values.
\item[\code{modelStruct}] an object inheriting from class \code{gnlsStruct},
representing a list of model components, such as \code{corStruct} and
\code{varFunc} objects.
\item[\code{groups}] a vector with the correlation structure grouping factor,
if any is present.
\item[\code{logLik}] the log-likelihood at convergence.
\item[\code{numIter}] the number of iterations used in the iterative
algorithm.
\item[\code{plist}] 
\item[\code{pmap}] 
\item[\code{residuals}] a vector with the residuals.
\item[\code{sigma}] the estimated residual standard error.
\item[\code{varBeta}] an approximate covariance matrix of the
coefficients estimates.
\end{ldescription}
\end{Value}
\begin{Author}\relax
Jose Pinheiro \email{Jose.Pinheiro@pharma.novartis.com} and Douglas Bates \email{bates@stat.wisc.edu}\end{Author}
\begin{SeeAlso}\relax
\code{\Link{gnls}}, \code{gnlsStruct}\end{SeeAlso}

